In electrical grids, a power supply fault triggering a circuit breaker to open must first be isolated through the identification of the relevant switching devices and the systematic opening of those devices. Only once the fault is isolated, can electricity supply be restored to the remaining customers or equipment electrically connected to the feeder. This is done through the closing of switching devices that isolate the faulty feeder from another feeder, effectively making a portion of the faulted feeder an extension of the good feeder. Complicating this process is the need to ensure that the power consumed by customers on a given segment of the feeder does not exceed the maximum capacity of the feeder.
Determining a sequence of switching devices to be opened or closed is the domain of Outage Management Systems under guidance by utility operators. The process to determine that sequence is non-trivial and is performed through the sub-field of artificial intelligence systems known as automated planning Current Outage Management Systems define the planning problem in terms of two potential actions that can be applied to any switching device in the grid—to open the device (preventing electrical current flow), or to close the device (allowing electrical current flow). The planner uses heuristics based on the power consumed by customers on the feeder and the maximum supported power on the feeder to decide which devices to open or close. This is the extent of the power restoration benchmark used in biennial automated planning competitions conducted as the “International Competition on Knowledge Engineering for Planning and Scheduling” or ICKEPS competitions. Search algorithms to solve this benchmark area an active area of research. The complexity of the search is at least O(n log n) which implies that a human taking 5 minutes to evaluate each state of a search for an outage covering 1000 locations could expect to complete their analysis in 24 complete days.
Different restoration search programs exist. These analyze a grid, its constraints and the known outages. Based on the constraints, recommendations are made for instance on routing decisions to restore areas and optimized for instances for the greatest number of restored customers per restoration action. As discussed earlier, while the routing optimization is known, DR methods are currently not part of these analyses. One restoration routing method is disclosed in “Strategic Planning for Power System Restoration” by Coffrin et al. p. 180-187, Proceedings of the International Conference on Vulnerability and Risk Analysis and Management (ICVRAM 2011) and the International Symposium on Uncertainty Modeling and Analysis (ISUMA 2011), held in Hyattsville, Md., Apr. 11-13, 2011, which is incorporated herein by reference in its entirety.
In a so-called Smart Grid, the electrical grid gains the ability to have two-way communication between customers on the line and the electrical distribution utility company. This provides several important features to the Outage Management System, among which are: 1) accurate power consumption measurements of customers on the line, and 2) the ability to modify the power consumption of customers on the line by issuing what are known as Demand Response events.
One known application of Demand Response is to prevent outages by load management as for instance described in a Siemens® Sell Sheet on its Demand Response Management System dated Oct. 18, 2012 of Siemens Industry, Inc. of Minnetonka, Minn.
It is believed that currently no Demand Response solutions exist to restore power to consumers who have lost power and restoration is hampered by capacity issues in grid equipment as happened in the Indian Point Unit 3 Nuclear power shutdown of 2012.
How Outages are Handled Currently
Power companies employ a wide range of tactics to handle an outage situation such as those caused by large storms. Depending on the size of the storm, a company may bring in restoration crews from surrounding areas. Prior to Hurricane Sandy, PSE&G requested more than 1300 linemen from other areas. Afterwards, there were more than 60,000 linemen and utility staff and an extra 3000 bucket trucks, involved in restoring power to 8.5M homes (2M of which were PSE&G customers).
Protection devices shut down power by separating circuits to prevent further damage, and restoration then involves identifying the damage then repairing the damage and then restoring power. In this process, telemetry is processed; and work crews are dispatched to locations of known damage or outage.
Managing so many resources requires talented dedicated teams of operators and human communication between control centers. Some Outage Management System software helps with customer communications, and some help with deploying work crews. However, ultimately operators must apply their expertise to prioritize locations.
Reduced Power is a Real Scenario
During Sandy, Entergy in NYC had to shut down Unit 3 at Indian Point Energy Center removing 1041 MW from the grid. Unit 3 serves a million customers. In a robust electrical system, it is better to serve less power to more customers than create a blackout. Substations that have multiple incoming feeders are common.
A substation that loses power on one feeder must transfer load to the feeders that are still energized. This can be done remotely if telemetry is available and no repairs are required. However, if the combined load will be excessive, the transfer cannot be accomplished.
Demand Response is a Real Capability
The demand response capability can reduce the load consumed by energized consumers. Currently most demand response participants operate in a manual mode whereby they may be informed by telephone that they need to power down certain devices. The FERC national action plan for Demand Response includes language encouraging the deployment of automated response systems. It is expected that automated DR systems will be deployed widespread in the near future.
Demand Response is Currently not used in Operator Based Outage Restoration Today
Demand Response is not typically used in developing a restoration strategy for work crews during an outage, to determine how to bring people back online, instead it is used in grid stabilization—simply ensuring that supply meets demand. (FP&L 2008). Operators must still carefully determine a restoration plan. The current manual process requires the operator to use his or her judgment to determine which sites to send work crews to identify or repair damage. This is a complicated procedure due to several factors, including (a) The analysis of supply and prioritization of types of customer and demand served by each outage area; (b) Distances to each outage; (c) There may not be enough work crews available to fix all problems at the same time; (d) It is not efficient to have work crews available at all times for the maximum number of simultaneous outages; (e) Cascading outages prevent repairs upstream until downstream repairs are complete; and (f) Current work crew locations.
To demonstrate the complexity of this process, one can equate solving this problem to a weighted graph search, in which the operator must analyze both the structure of the network and establish goals (weights) for repairing outage locations. This is a typical search problem on a weighted undirected connected graph for which search algorithms exist with O(E log V) time complexity; here E is the number of transmission lines, and V is the number of outage locations. This is a minimum, but the fact that there are multiple work crews, and dependencies between outage locations complicate the search process.
For small grid networks of outages, the search can be executed by a human in a reasonable time. Due to the minimum O(n log n) complexity, the problem scales non-linearly; for example at 1000 outage locations, there are 6908 steps to complete the search. At 5 minutes to analyze each outage location, the search would take 24, 24-hour days to complete, which is clearly not acceptable in addressing real-life outage situations.
Thus outage restoration is best served by heuristic search software that models the structure of the electrical network and can perform this analysis in real time. Furthermore if the operator wishes to apply the demand response methodology described herein, then the operator must at each solution communicate with the selected consumers for power restoration. As discussed above, the number of consumers could easily be in the 1000's. For example, a 10 kw drop on a 1 MW house would require 100 demand response notifications to be communicated to end users, before 1 additional house can be brought online (1 customer for 100 1% drops). Clearly a 10 MW drop on a 200 MW industrial site would be another matter (10 customers could be brought online for a 5% drop).
Accordingly, novel and improved methods and systems are required to apply Demand Response methods for restoring power in parts of a utility grid with capacity constrained equipment after a power outage.